168 results about "Fuzz testing" patented technology

The invention provides a protocol security fuzzy test system and method based on a speed reduction bridge, the speed reduction bridge is integrated on an FPGA board card, and the speed reduction bridge comprises a protocol fuzzy test processing module, a clock domain crossing buffer and flow control module and a programmable time sequence fault injection circuit. The protocol-based fuzzy test processing module is used for detecting feature information and state information of a data stream flowing through the speed reduction bridge and outputting an action instruction; and based on different action instruction types, driving different modules to inject different types of abnormities into the data stream, and realizing fuzzy testing based on the abnormal data stream. The fuzzy test processing module is embedded in the FPGA logic, different rules can be matched according to the feature information of the data flow, different abnormal injection of the data flow is achieved, the abnormal injection opportunity is determined according to the link state information, the speed reduction bridge has the dual functions of normal protocol relaying and active abnormal injection at the same time, and the reliability of the speed reduction bridge is improved. The test of various abnormities of the equipment to be tested can be realized.

The invention discloses a fuzzy test verification method and system for a password service interface, and belongs to the technical field of password service technologies and software testing. The method comprises the following steps: firstly, constructing a password service interface feature library, and extracting key feature information; designing a multi-dimensional fuzzy test variation strategy based on the password service interface feature library, and generating a fuzzy test case set covering grammar exception, semantic exception and encryption scene exception; realizing communication adaptation between a test case and a target password service interface through a dynamic adaptation module, executing a fuzzy test, and collecting information such as response data and an operation state of the interface in real time; and finally, carrying out deep analysis on the acquired information through an anomaly detection and analysis module, positioning a vulnerability type and a trigger path, and generating a standardized test report. According to the method, different types of password service interfaces can be accurately adapted, the test coverage and the vulnerability discovery efficiency are remarkably improved, and reliable support is provided for security verification of the password service interfaces.

The invention relates to the cross technical field of artificial intelligence and software security testing, in particular to a deep learning framework fuzz testing method based on large model cue word optimization, which is used for improving vulnerability mining efficiency and testing intelligence level of fuzz testing on a deep learning framework. According to the method, the advantages of a large language model in the aspects of code understanding and generation are fully utilized, and efficient vulnerability detection of a deep learning framework is realized by introducing a cue word adaptive optimization and variation mechanism. The method mainly comprises the following steps: (1) providing a deep learning framework API classification method and a cue word routing mechanism; (2) proposing a large model cue word adaptive optimization mechanism; and (3) proposing a deep learning framework fuzzy test variation strategy and a dynamic selection mechanism. According to the method, the automation and vulnerability discovery capability of fuzzy testing can be remarkably improved while the generation quality is ensured, and the method has relatively high universality and application value.

The embodiments of this disclosure provide a kernel fuzzing method and apparatus based on unicorn simulation. The method includes: creating a unicorn engine in the harness of unicorefuzz, mapping all required memory regions and setting the initial values ​​of each register according to the needs of the target program; passing the fuzzing input data generated by AFL to the unicorn engine; after the user specifies the target function address and breakpoints for the fuzzing, the unicorn engine starts the AFL fork server to simulate the execution of the fuzzing of the target program; and loading memory as needed and handling abnormal situations of the target program during the fuzzing process.

The application discloses a concurrent program vulnerability detection method based on fuzzy testing and static analysis, and comprises the following steps: compiling a to-be-detected program source code, obtaining a bytecode file according to the compiling, then performing static analysis to obtain a concurrent vulnerability pair; performing program insertion on the concurrent vulnerability pair to obtain a program binary file with inserted code; taking the program binary file with inserted code as a test program, adding an initial input file in an input queue, adding a concurrent vulnerability-oriented mechanism in AFL and adding a judgment mode of whether an input is interesting, and pruning unfeasible paths; performing fuzzy testing to realize vulnerability detection. The application can not only utilize the characteristics of concurrent program vulnerabilities, use an optimized redundant node static analysis technology to reduce the number of concurrent vulnerability detections, but also use a fuzzy testing technology to efficiently reproduce the occurrence process of the concurrent program vulnerabilities, so that more efficient and more intuitive concurrent vulnerability detection is realized.

The application discloses an industrial control system protocol fuzzing test method based on state conversion, and particularly, the communication flow of an upper computer and a PLC is analyzed, a protocol finite state machine of a communication interaction process is constructed, and the PLC is subjected to the fuzzing test by using test cases violating the finite state machine in a test process, so that the purpose of triggering unknown vulnerabilities of the PLC is finally achieved. The application uses the protocol finite state machine to guide the state conversion path of the fuzzing test process. The method for obtaining the test cases violating the original protocol finite state machine and then performing the fuzzing test on the equipment can efficiently find vulnerabilities. The confirmation method for the state conversion leading to the vulnerabilities can obtain the shortest state conversion path leading to the vulnerabilities.

A coverage-guided code generation and fuzzing method based on a large model is proposed to address the problems of existing large model fuzzers, which not only ignore the guiding role of coverage information in the fuzzing loop but also generate prompts too hastily. This invention comprises four stages: prompt generation, fuzzing loop, mutation operator selection, and vulnerability detection. This method proposes a prompt generation method based on expert templates and a fuzzing loop method guided by unequal interval coverage information. The former lowers the barrier to entry for users of CoverFuzz and improves the quality of initial prompts; the latter, guided by coverage information, better covers the object under test to detect vulnerabilities. This invention significantly improves the effectiveness of programs generated by fuzzing based on large models, solves the problem of existing large model-based fuzzers ignoring coverage information, and also achieves higher code coverage.

The application discloses a fuzzy test seed screening method and device. The method is based on multiple sets of fuzzy test seeds. Seed messages corresponding to the fuzzy test seeds are sent to a test object in sequence, and the change degree of each state feature of the test object when processing the seed messages is monitored. The change degree of the state of the test object when processing the messages can determine the complexity of the device under test when processing the messages or commands. The more complex the processing logic is, the more likely bugs exist, and the test should be focused on. Finally, the fuzzy test seeds are sorted according to the state influence degree of the seed messages on the test object, so as to determine the screening priority of each fuzzy test seed, so that the fuzzy test seeds with higher priority are screened preferentially to generate test cases, thereby improving the test efficiency.

A security inspection device (100, 100A, 200, 300) comprises: a switching unit (111) that switches between enabling and disabling of a vulnerability mitigation function (specific function; SSP and ASLR) issued by an OS (120) to a target program (140); a search unit (112) that, in a state in which the mitigation function for vulnerabilities of the target program (140) is enabled, employs fuzzing to search for input information with which an output differs each time the target program (140) is executed, despite the input information being the same; and a confirmation unit (113) that, when the input information found by the search unit (112) is input to the target program (140) in a state in which the mitigation function is disabled, confirms whether or not randomness remains such that the output differs each time the target program (140) is executed.

This invention discloses an IoT fuzzing method based on LLM-guided and FSM dynamic inference, belonging to the field of IoT network security and software testing technology. Addressing the problems of low coverage and inaccurate state machine inference in current IoT protocol fuzzing, this invention first constructs an initial FSM by combining IoT protocol specifications and captured traffic data. Then, it generates a large number of test cases through mutation of seed test cases for fuzzing testing. Features are extracted from device responses, and state identification is performed by calculating similarity. When a new state appears, the FSM and state fingerprint database are updated. When coverage becomes a bottleneck, LLM-guided path inference is used to generate extended sub-FSMs and test cases, which are then executed. The FSM is then corrected based on the test results. This invention enables high-precision automated construction of IoT protocol state FSMs, improving test coverage and enhancing the efficiency and accuracy of vulnerability discovery.

This application proposes a fuzzing method and system for CXL devices based on hierarchical state abstraction. While accurately capturing cross-layer state changes in CXL to effectively discover vulnerabilities, it reduces the amount of state data through hierarchical feature selection, reduces deduplication overhead through incremental hashing, and reuses intermediate state operation sequences through a shared state pool, thereby shortening the average execution time of each test case and improving the overall throughput of fuzzing. This application significantly shortens the average test cycle while maintaining a high vulnerability detection rate, especially demonstrating a significant improvement in coverage efficiency for multi-hop memory consistency protocol scenarios.

This invention discloses a code defect analysis and repair method and system based on a large language model. The method includes: receiving crash information triggered by fuzzing or program execution, and performing crash analysis to obtain crash analysis results; obtaining key defect analysis information based on the crash analysis results, and constructing structured defect analysis information based on the key defect analysis information; inputting the structured defect analysis information into a defect analysis model, and performing reasoning analysis on the code defects according to a three-round reasoning strategy to obtain defect analysis results; searching a historical repair database based on the defect analysis results to obtain historical repair retrieval results, and inputting the defect analysis results and historical repair retrieval results into a patch generation model to generate candidate patch code; performing sandbox verification on the candidate patch code to obtain valid patch code and patch verification results, and outputting an analysis and repair report based on the defect analysis results, valid patch code, and patch verification results.

The application discloses a kind of efficient regional fuzzy test methods with vulnerability as guide, including the following steps: S1, the target program with vulnerability as guide is plugged in and is compiled, and the target program after plugging in containing code coverage information and potential vulnerability position information is obtained;S2, the target program after plugging in is run regional fuzzy test by the core link of fuzzy test based on seed selection;S3, according to the corresponding processing of regional fuzzy test running result;S4, after the end of regional fuzzy test running, target seed is screened and deduplicated, and potential vulnerability analysis is carried out to the seed after screening and deduplicating.The application can effectively reduce the number of seeds in seed queue in the process of regional fuzzy test while keeping the coverage basically unchanged, reduce the cost of fuzzy test, and then improve the efficiency of fuzzy test.

The invention provides an automatic network protocol program de-randomization method, and belongs to the technical field of fuzzy testing. The method comprises the following steps: a code analysis stage: analyzing a target network protocol program by constructing a prompt guide large language model, identifying random problems and classifying the random problems; an adaptive query generation stage: dynamically generating an adaptive query for each class of deterministic problems so as to locate random codes involved by the random problems; in the deterministic transformation stage, a deterministic patch containing deterministic constraints is generated according to the random codes; in the patch verification stage, the validity of the deterministic patch is verified by executing path consistency detection and fuzzy test efficiency evaluation; and a tool chain integration stage: integrating the generated deterministic patch into a fuzzy test framework so as to carry out continuous verification and subsequent optimization. According to the method, the randomness problem in the program can be identified and eliminated, and the stability and effectiveness of the fuzzy test are remarkably improved.

FIG. 1B 20 25 27 13 35 26 N ov 2 02 5 3 5 M E T H O D A N D S Y S T E M F O R D E T E R M I N I N G O N E O R M O R E F R O N T I E R 2 0 2 5 2 7 1 3 3 5 2 6 N o v 2 0 2 5 B R A N C H E S D U R I N G S O F T W A R E T E S T I N G A B S T R A C T F I G . 1 B Testing system 121 Processing system 123 Fuzzer 125 Look-up database 127 Visualizer 129 Software binary files 120 FIG. 1B Look-up database 127 Second list binary search tree 139 First list binary search tree 133 Third list 135 Fourth list 141 First list 131 Second list 137 FIG. 1C 2 / 9 Fuzzer 125 Visualizer 129 files 120 FIG. 1B search tree 133 Third list 135 Fourth list 141 search tree 139 FIG. 1C 20 25 27 13 35 26 N ov 2 02 5 2 / 9 2 0 2 5 2 7 1 3 3 5 2 6 N o v 2 0 2 5 F I G . 1 B F i r s t l i s t 1 3 1 S e c o n d l i s t 1 3 7

The application discloses an MCP server security evaluation method and device, a storage medium and an electronic device, and relates to the technical field of computers. The method comprises the following steps: performing static analysis on code data of an MCP server according to context information of the MCP server, and obtaining a static analysis report; sending a fuzzing test request to the MCP server to perform dynamic testing, and analyzing whether response data of the MCP server includes reverse attack information for a client using a large model, and obtaining a dynamic analysis report; obtaining an attack scene adapted to the MCP server and generating a test case, performing scene simulation testing on the MCP server based on the test case, and obtaining a simulation analysis report; and comprehensively evaluating the security of the MCP server based on the static analysis report, the dynamic analysis report and the simulation analysis report. The application fuses semantic understanding during static analysis, detects whether a client is reversely attacked during dynamic analysis, and focuses on simulating real attacks during simulation testing, so that the security of the MCP server can be evaluated.

This application relates to a fuzz testing visualization method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: acquiring program information of a target test program; the target test program includes at least one function; the program information indicates the number of basic blocks contained in each function and the number of transition paths between basic blocks; generating an overall program graph corresponding to the target test program based on the program information; wherein the overall program graph includes graphical elements representing each function; each graphical element includes graphical sub-elements representing basic blocks in the corresponding function; acquiring a fuzz test log corresponding to the target test program, recording the coverage of basic blocks by the fuzz test seed during fuzz testing of the target test program; color-coding the graphical sub-elements in the overall program graph based on the fuzz test log to generate a program state graph. This method can improve fuzz testing efficiency.

The invention discloses a parameter sensitive database fuzzy testing method and system based on combination testing, and the method comprises the steps: obtaining a coverage basic block set of each parameter through static taint analysis, generating a weight value of each parameter, and further determining a priority parameter combination set; training the condition generation probability of each SQL characteristic class under specific parameters through coverage rate feedback, and constructing a test sample generation scheme corresponding to the parameters; and finally, generating parameters and corresponding test samples through the weight data generated in the training stage, and checking execution results of the test samples through an oracle machine. According to the method, the parameter weight and the characteristic generation probability are adjusted through static analysis and the code coverage rate, intelligent scheduling of high-value test paths is achieved, the test efficiency and the coverage rate are improved, meanwhile, the problem of parameter combination space explosion is solved, and an innovative solution is provided for quality assurance and performance optimization of a database system.

This invention discloses a method for mining image and video format vulnerabilities based on fuzzing. First, an input module obtains image and video input files from the user's seed library. Then, parallel fuzzing is performed on the image and video input files using master-control and slave modes to uncover image and video format vulnerabilities. Finally, a simplification module monitors the queue directory of each instance in the master-control and slave modes and simplifies the queue directory set to provide simplified input data for subsequent fuzzing. This invention effectively solves the problems of non-linear processing speed growth and corpus duplication in existing AFL fuzzing frameworks by using improved measures such as parallel processing of multiple master-control modes and simplification of the corpus set during fuzzing.

The application discloses a kind of fuzzy test method and process based on deep learning model.First, different formats of Linux application are collected, and the application collected and seed file with the same format are saved;Second, the variation model under fixed variation strategy is constructed based on value-oriented DQN algorithm and strategy-oriented TRPO algorithm, and the improved Thompson sampling algorithm is used to construct Havoc random variation model, to output the effective variation operator and variation strategy chain information of test sample;Then, the AFL source code is modified, the two proposed variation models are embedded, and the interaction between the model and AFL is realized using shared memory;Finally, for the AFL embedded with the model obtained in the last step, an automated execution script is written and its execution time is set, and the afl-fuzz, afl-showmap and afl-cov instructions are executed to train the embedded two models and perform automated fuzz testing, and according to the fuzz testing execution result, the code coverage, the number of crashes, the use of variation operator and other results are visualized.

This application relates to a batch fuzz testing method, apparatus, computer device, and readable storage medium. The method includes: for each round of testing, obtaining the mutated packets required for that round of testing; generating abnormal test packets required by the corresponding device under test for that round of testing based on the mutated packets and the network address of each device under test; sending each abnormal test packet to the corresponding device under test through the switch; and monitoring the response of each device under test to the received abnormal test packets; and determining the anomaly resistance capability of the corresponding device under test based on the response of each device under test in each round of testing. This application can reduce hardware costs and achieve batch parallel testing to improve testing efficiency.

The invention relates to a test corpus acquisition method and device based on a function call sequence, computer equipment, a computer readable storage medium and a computer program product, relates to the field of software testing, and can improve the generation efficiency of a corpus database. The method comprises the steps that calling track information of function calling in the running process of a target program is obtained, and a function calling sequence is determined based on the calling track information; obtaining a target execution state of the target program, and determining a coverage rate identifier corresponding to the target execution state in the target program; and based on the function call sequence and the coverage rate identifier, performing a fuzz test to obtain a coverage rate test result, adjusting a test strategy for the current function call sequence according to the coverage rate test result, and performing the fuzz test again until the coverage rate test result meets a preset coverage rate condition, and adding the function call sequence to a test corpus corresponding to the target execution state.

The invention discloses an LLM-based fuzzy test initial seed generation method and system, a computer and a storage medium. Lightweight static analysis is performed on target software source codes, an expansion function call graph is constructed, two key features of hard coding constraint and security sensitive operation are extracted, and explicit tags are injected in situ into the source codes; a semantic aggregation strategy based on a recursive sub-function abstract is adopted, the sub-function source code injected with the label is converted into a semantic abstract from bottom to top through a large language model, and a global semantic abstract retaining key constraint information is generated through hierarchical cue word construction; constructing a seed generation task containing a thinking chain guide instruction, forcing the model to perform reasoning according to logic steps of constraint solution, abnormal construction and enhanced generation, and generating a targeted initial input sample (namely, an initial seed) for fuzzy testing; and performing static validity check on the grammar by utilizing a general format analyzer, and performing fault-tolerant processing by combining a multi-stage backspacing mechanism. Through the recursive abstract aggregation and thinking chain reasoning technology, deep semantic understanding of complex code logic and automatic generation of high-coverage-rate test cases are achieved.

The application discloses a neural machine translation Internet of Things remote proof method for data flow attacks, which comprises an offline stage and a runtime verification stage. In the offline stage, a verifier and a prover first complete the negotiation of a symmetric key for subsequent remote proof, and at the same time, complete static plugging for a target program. A program control flow dataset is pre-constructed through fuzzy testing, a neural machine translation model is trained to establish the mapping of program input to an execution path, and a control flow graph is embedded to provide a structured prior for decoder attention. In the runtime verification stage, the verifier initiates a proof challenge to the prover, the prover provides the program input and the control flow path of the last execution, the verifier predicts a benign path from the program input by the neural machine translation model, and the difference between the benign predicted path and the actual path is used to judge the legitimacy of the prover. The application realizes accurate modeling of the program execution path, and shows effective detection capability for the abnormal path triggered by malicious input containing real vulnerabilities, and a good balance is achieved between detection coverage and running overhead.

The application relates to the technical field of network security and software testing, and discloses a generative fuzzy testing method for a PDF reader, which has the technical effects of reducing the cost of syntax modeling, improving the coverage of the model, and effectively suppressing the hallucination of a large language model by designing a polymorphic object syntax reduction template and automatically constructing a POG reduction library by combining a retrieval enhancement generation technology; wherein the POG template encodes the complex semantics of the PDF specification into a machine-interpretable structured skeleton by introducing inheritance, polymorphism and dynamic generation instruction fields; the RAG technology combines the specification text, the table and the prior knowledge of the large model to automatically extract accurate object definitions from the ISO standard document; and finally, the POG reduction library with high coverage and high fidelity is constructed by combining the breadth-first iterative extraction and manual checking, thereby fundamentally solving the problems of high cost of traditional manual modeling, poor structural consistency of the deep learning method and hallucination caused by direct generation of the large language model.

The invention discloses a fuzzy test seed generation method and system based on anti-fact deduction and large language model cooperative driving, and belongs to the technical field of intelligent fuzzy test. The fuzzy test seed generation system receives function description and example codes provided by a user, semantic modeling is carried out through an input simplification module, and cue words are generated. And then the prompt words are input to a vulnerability code generation module, a plurality of code candidates are generated by generating large model reasoning in combination with a target function, score screening is carried out, and high-score codes are selected from the code candidates to serve as a semantic basis for subsequent seed generation. The seed generation module analyzes the high-score codes, and high-quality seeds are obtained by generating a large model. The fuzzy test seed generated by the method has high pertinence, input aggressiveness and test correlation are improved, and the method is widely applicable.

The application provides a fuzzy test method for multimodal large model application. According to a preset test requirement, a corresponding test mode is selected as a target test mode, and under the target test mode, a test input content is constructed based on a preset prompt word library to test the application model under test. Meanwhile, based on a 3D label mapping algorithm, dimension label processing is performed on the test output content to obtain target test output content including a label dimension. Finally, according to the label dimension of the target test output content, the risk level of the application model under test is determined, so that model test of the application model under test is realized.

Embodiments of this disclosure provide a fuzz testing method, apparatus, device, and computer-readable storage medium. The method includes: determining a fuzz testing target; sequentially invoking corresponding brute-force dictionaries according to a preset dictionary invocation strategy; and testing the fuzz testing target based on the corresponding brute-force dictionaries. In this manner, different brute-force dictionaries can be invoked to automatically test the fuzz testing target, thereby improving the flexibility of weak password testing methods.

The invention relates to the technical field of software testing, and provides an automatic system call specification generation method and system for kernel fuzz testing. The method comprises the following steps: a static analysis stage: compiling a Linux kernel source code to be tested into an LLVM byte code, and performing deep static analysis on the LLVM byte code to identify and reconstruct an interface and a parameter type called by a system; a symbol execution and specification generation stage: adopting a constraint extraction and solution algorithm to extract constraints under different paths, carrying out constraint solution, and generating an initialized calling specification template in combination with an interface called by the system, the parameter type and the constraints; a standard test and verification stage: constructing an evaluation system of a multi-dimensional index to verify the initialized calling standard template and generate corresponding error information; in the large language model auxiliary correction stage, the initialized calling standard template and the error information serve as input, and a high-quality calling standard template is obtained after a large model iteration repair algorithm is conducted.